OhioHealth needed technology that would enhance coding performance while minimizing the need to add staff.

F

ounded in 1891, OhioHealth (www.Ohio Health.com) is a network of eight not-for-profi t hospitals and numerous healthcare organizations serving patients in central Ohio. Growth in patient volume, regulatory changes and the acquisition of a number of physician practices have increased the demands on OhioHealth&#x2019;s coding personnel, neces- sitating a cost-effective tool that would signifi cantly increase coder productivity while also improving the clean-claims rate. Diane Setty, RHIA, CPHQ, OhioHealth&#x2019;s corporate director of HIM, was looking for technology that would enhance coding performance while minimizing the need to add staff. She needed a tool that would dramatically improve coder productivity, especially in light of the impending ICD-10 implementation. Compounding the situation, competition for capital with the clinical departments meant that the recommended investment must provide strong, measureable, positive returns. For many years, Setty had been keeping her eye on computer-assisted coding (CAC) as a tool with the potential to hyper-boost productivity. She spent hours talking to a number of CAC vendors getting a clearer understanding of the technology and their offerings. She now believed the industry had matured enough that she could advise her executive team on the advantages of this new product.

The challenge

Evaluating new technology is a daunting process. Coupled with this was convincing the coding supervi- sors and coders that this application would make them more effi cient &#x2013; and that they could trust its accuracy. Also, capital funding did not become available for this project until eight weeks before the end of their fi scal year &#x2013; and it would have to be spent before the fi scal year&#x2019;s end.

Setty and her team had already conducted the vendor evaluation and selection process, which enabled rapid implementation of the fi rst phase of a CAC software deployment. In addition to ensuring a strong ROI, the selected software would have to make coders more effi cient and more compliant, improve their medical

12 February 2011 The solution

The offerings of six prominent CAC vendors were reviewed; this was reduced to three potential contend- ers for more in-depth evaluation. The team burned the midnight oil further researching articles on the CAC industry and technology. They read product literature and conducted vendor discussions to identify those that best met their minimum requirements. A matrix was created to compare vendor functionality against the team&#x2019;s criteria.

&#x201C;There were a number of reasons we selected A- Life,&#x201D; explains Setty. &#x201C;We had seen their NLP engine in action at University of Pittsburgh Medical Center and knew it had the capability to do what we needed.

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necessity by reducing claim denials and boost their case mix for inpatient services.

The software would also have to accommodate the current workfl ow process, which meant it would have to integrate seamlessly with the existing legal medical records solution. Medical-necessity checking would have to be embedded in the product so that coders could access it in real time from within the application without having to exit one system and enter another. Additionally, the team had to address coders&#x2019; quality concerns &#x2013; and how the technology would affect their lives and workloads.

The software would have to accommodate the current workfl ow process, which meant it would have to integrate seamlessly with the existing legal medical records solution. Medical-necessity checking would have to be embedded in the product so that coders could access it in real time from within the application without having to exit one system and enter another.